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Stacked autoencoder with weighted loss function for intrusion detection in IoT application

Multimedia Tools and Applications 2024年 1-29页

作者： Gangula, Rekha Vutukuru, Murali Mohan Kumar, M. Ranjeeth Department of Computer Science and Engineering Vaagdevi Engineering College Telangana BollikuntaWarangal506005 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur522502 India School of Computer Science & amp Artificial Intelligence SR University Anantasagar Telangana Warangal506371 India

The fast increase of network traffic in recent times causes significant detection of intrusions in Internet of Things (IoT) environments. Currently, Deep Learning (DL) models play a crucial role in cyber security for malicious identification and intrusion detection in IoT networks. The existing methods have drawbacks like overfitting, data imbalance, and not completely capturing complex dependencies and relationships among input and features that are significant for intrusion detection. To overcome these limitations, Stacked Autoencoder (SAE) with weighted loss function is proposed for effective Intrusion Detection System (IDS). The SAE includes weighted loss function to minimize the overfitting issue and finally, the One Class-Support Vector Machine (OCSVM) is used in the classification layer to classify the intrusions. The database used for intrusion detection in IoT environment are Bot-IoT and ToN-IoT databases, which undergo pre-processing by using standard scalar and min–max normalization to remove duplicates and inconsistent data in the databases. Then, the optimal features are selected by using firefly optimizer which selects active features values for classification. In the resulting phase, the stacked autoencoder obtains 99.99%, and 99.70% of classification accuracy on the Bot-IoT and ToN-IoT databases, respectively, which are superior while compared to the traditional autoencoder model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Support vector machines

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An Initial Risk Assessment for Multimodal with LSTM-Based Trust Evaluation Framework for Autonomous Vehicle Security

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SN computer Science 2025年第2期6卷 1-15页

作者： Renjith, P.N. Balasubramani, S. Ramesh, K. Patnala, Eswar School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

This research aims to develop a new approach to increase the safety and reliability of Autonomous Vehicle (AV) through the proposed risk assessment framework, supported by the trust evaluation approach derived from a machine learning algorithm. This work proposes a comprehensive risk assessment model with real-time sensor data, Vehicle-to-Vehicle (V2V), key communication reliability, environmental cognition, and Improved Long Short-Term Memory (I-LSTM). This framework is multidimensional compared to most conventional models, focusing primarily on assessing individual threat factors. Here, the machine learning portion is designed to evaluate and predict trust by quickly processing vast amounts of information from sensors, signals, and prior operation history. Such an approach to trust assessment enables the system to continuously evaluate the dependability of automated vehicle operation and identify threats to security with improved accuracy. The effectiveness of the performance of this method is substantiated by a number of stand-alone and live scenarios, where the model's performance is higher in perceiving possible risks, avoiding threats, and enhancing safety than the conventional approaches. The findings suggest that the safety regulations of the AVs will be improved by using a multi-dimensional risk assessment model and integrating the I-LSTM trust rating with 95.5% accuracy, 96.7% risk identification, and 180 ms response time. This will cause AV and its surrounding environment to be accepted by the public and used on the roads. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Autonomous vehicles LSTM model Machine learning Risk assessment Trust evaluation

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Inpainting Technique for Removing Vehicle Logos Using Haar-Like and Fast Marching Methods 8

Inpainting Technique for Removing Vehicle Logos Using Haar-L...

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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Durmus, Meryem Ergen, Burhan Durmus, Omer Atasoy, Ferhat Samsun University Center for Distance Education Samsun Turkey Firat University College of Engineering Department of Computer Engineering Elazig Turkey Samsun University College of Engineering Department of Software Engineering Samsun Turkey Karabuk University College of Engineering Department of Computer Engineering Karabuk Turkey

ISBN: (纸本)9798331531492

Inpainting is the process of removing an unwanted object or region in an image without degrading the image quality. Inpainting offers a solution to problems such as repairing damaged images or removing unwanted parts of the image. In this study, we focus on the detection of brand logos in vehicle images and the removal of these regions using inpainting. The study consists of two main phases: First, the Haar-like features method is used to detect vehicle logos. Then, the detected logos are removed from the image by applying inpainting with the Fast Marching Method. As a result of the experimental studies, it was found that the methods successfully detected and removed the logos with an accuracy of 98%, and the PSNR value ranged between 25.36 dB and 25.41 dB, and the SSIM value ranged between 0.96692 and 0.96994 while maintaining the image quality. These results show that the proposed method exhibits high performance in removing vehicle logos efficiently. © 2024 IEEE.

关键词： fast marching method haar-like inpainting logo recognition

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AI-Driven Resource and Communication-Aware Virtual Machine Placement Using Multi-Objective Swarm Optimization for Enhanced Efficiency in Cloud-Based Smart Manufacturing

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computers, Materials & Continua 2024年第12期81卷 4743-4756页

作者： Praveena Nuthakki Pavan Kumar T. Musaed Alhussein Muhammad Shahid Anwar Khursheed Aurangzeb Leenendra Chowdary Gunnam Department of Computer Science&Engineering Koneru Lakshmaiah Education FoundationGuntur522302India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of AI and Software Gachon UniversitySeongnam-Si13120Republic of Korea Department of Electronics and Communication Engineering SRM UniversityAmaravati522502India

Cloud computing has emerged as a vital platform for processing resource-intensive workloads in smart manu-facturing environments,enabling scalable and flexible access to remote data centers over the *** these environments,Virtual Machines(VMs)are employed to manage workloads,with their optimal placement on Physical Machines(PMs)being crucial for maximizing resource ***,achieving high resource utilization in cloud data centers remains a challenge due to multiple conflicting objectives,particularly in scenarios involving inter-VM communication dependencies,which are common in smart manufacturing *** manuscript presents an AI-driven approach utilizing a modified Multi-Objective Particle Swarm Optimization(MOPSO)algorithm,enhanced with improved mutation and crossover operators,to efficiently place *** approach aims to minimize the impact on networking devices during inter-VM communication while enhancing resource *** proposed algorithm is benchmarked against other multi-objective algorithms,such as Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),demonstrating its superiority in optimizing resource allocation in cloud-based environments for smart manufacturing.

关键词： Resource utilization smart manufacturing efficiency inter VM communication virtual machine placement cloud computing multi-objective optimization

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Enhancing Protein Structure Generation Through Deep Learning Techniques 3

Enhancing Protein Structure Generation Through Deep Learning...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Nagaraj, P. Sudar, K. Muthamil Sri, Vattikuti Sowjya Nikitha, V. Reddy, Vennapusa Sujay Sai Vikram, M. Kalasalingam Academy of Research and Education Krishnankoil Department of Computer Science and Engineering Virudhunagar India Mepco Schlenk Engineering College Department of Computer Science and Engineering Virudhnagar India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Virudhnagar India

ISBN: (纸本)9798350361186

Protein structure prediction is a critical task in molecular biology, with far-reaching implications for innovation, disease understanding, and drug discovery. Deep learning is one successful way to deal with this difficult issue. The purpose of this work is to investigate how deep learning technologies can be used to increase the precision and effectiveness of protein structure formation. We examine numerous deep learning models and approaches in depth, emphasizing their advantages and disadvantages in terms of protein structure prediction. Our study includes a comparison of current deep learning-based approaches, demonstrating their usefulness on both benchmark datasets and real-world examples. We discuss how deep learning models are built by using inter-residue interactions, secondary structure prediction, and sequence data. In addition, we investigate how generative adversarial GANs could be utilized to improve protein structure formation by using complex data distributions. We show how deep learning may dramatically improve the precision of protein structure prediction, ultimately producing more dependable models, through extensive experiments and case studies. In addition, we discuss how deep learning models may be interpreted and applied to the field of protein folding, providing an understanding of both their strengths and weaknesses. © 2024 IEEE.

关键词： Deep neural networks

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Real-Time Lie-Speech Determination Using Voice-Stress Technology

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer Science and Mathematics 2024年第2期5卷 81-93页

作者： Al-Dhaher, Fadi K. Mohammed, Duraid Y. Khalaf, Mohammed Al-Karawi, Khamis A. Sarfraz, Mohammad Al Maathidi, Muhammad Mazin Computer Engineering Department College of Engineering Al-Iraqia University Baghdad Iraq General Directorate of Education Anbar Ramadi31001 Iraq Computer Science Department Al-Maarif University College Ramadi31001 Iraq University of Diyala Iraq Electrical Engineering Department Aligarh Muslim University Aligarh India Computer Science Department University of Bahrain Bahrain

Lie detection has gained importance and is now extremely significant in a variety of fields. It plays an important role in several domains, including law enforcement, criminal investigations, national security, workplace ethics, and personal relationships. As advances in lie detection continue to develop, real-time approaches such as voice stress technology have emerged as a feasible alternative to traditional methods such as polygraph testing. Polygraph testing, a historical and generally established approach, may be enhanced or replaced by these revolutionary real-time techniques. Traditional lie detection procedures, such as polygraph testing, have been challenged for their lack of reliability and validity. Newer techniques, such as brain imaging and machine learning, might offer better outcomes, although they are still in their early phases and require additional testing. This project intends to explore a deception-detection module based on sophisticated speech-stress analysis techniques that might be applied in a real-time deception system. The purpose is to study stress and other articulation cues in voice patterns, to establish their precision and reliability in detecting deceit, by building upon previous knowledge and applying state-of-the-art architecture. The performance and accuracy of the system and its audio aspects will be thoroughly analyzed. The ultimate purpose is to contribute to the advancement of more accurate and reliable lie-detection systems, by addressing the limitations of old techniques and proposing practical solutions for varied applications. This paper proposes an efficient feature-selection strategy, which uses random forest (RF) to select only the significant features for training when a real-life trial dataset consisting of audio files is employed. Next, utilizing the RF as a classifier, an accuracy of 88% is reached through comprehensive evaluation, thereby confirming its reliability and precision for lie-detection in real-time scena

关键词： National security

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Artificial Intelligence Enabled Future Wireless Electric Vehicles with Multi-Model Learning and Decision Making Models

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Tsinghua Science and Technology 2024年第6期29卷 1776-1784页

作者： Gajula Ramesh Anil Kumar Budati Shayla Islam Louai A.Maghrabi Abdullah Al-Atwai Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering&TechnologyHyderabad 500090India Institute of Computer Science and Digital Innovation(ICSDI) UCSI UniversityKuala Lumpur 56000Malaysia also with Department of ECE KoneruLakshmaiah Education FoundationHyderabad 500090India ICSDI UCSI UniversityKuala Lumpur 56000Malaysia Department of Software Engineering College of EngineeringUniversity of Business and TechnologyJeddah 21448Kingdom of Saudi Arabia Department of Computer Science Applied CollegeUniversity of TabukTabuk 47512Kingdom of Saudi Arabia

In the contemporary era,driverless vehicles are a reality due to the proliferation of distributed technologies,sensing technologies,and Machine to Machine(M2M)***,the emergence of deep learning techniques provides more scope in controlling and making such vehicles energy *** existing methods,it is understood that there have been many approaches found to automate safe driving in autonomous and electric vehicles and also their energy ***,the models focus on different aspects *** is need for a comprehensive framework that exploits multiple deep learning models in order to have better control using Artificial Intelligence(AI)on autonomous driving and energy *** this end,we propose an AI-based framework for autonomous electric vehicles with multi-model learning and decision *** focuses on both safe driving in highway scenarios and energy *** deep learning based framework is realized with many models used for localization,path planning at high level,path planning at low level,reinforcement learning,transfer learning,power control,and speed *** reinforcement learning,state-action-feedback play important role in decision *** simulation implementation reveals that the efficiency of the AI-based approach towards safe driving of autonomous electric vehicle gives better performance than that of the normal electric vehicles.

关键词： wireless vehicles deep learning multi-model learning reinforcement learning Artificial Intelligence(Al)

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Design of Flora Inspired Savonius Wind Turbine 6th

Design of Flora Inspired Savonius Wind Turbine

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6th International Conference on Mechanical engineering Research, ICMER 2021

作者： Ladao, Apple Jee P. Bacus, Delan S. Mechanical Engineering Program College of Engineering Education University of Mindanao Matina Davao Philippines

ISBN: (纸本)9789811915765

Wind energy is a promising source of renewable energy. However, integrating energy harvester in an urban landscape poses a significant challenge. This study developed a wind turbine, a bio-inspired savonius that will blend with the environment. Savonius wind turbine is a vertical-axis wind turbine type used to convert wind force into torque on a rotating shaft. Using SolidWorks flow simulation, the different configuration was considered such as twist angle and guide vanes. Simulation shows a promising potential of this turbine. A scaled prototype was made and tested. Results showed that a 135° angle of twist with guide vanes is ideal and has the highest torque during its running condition in theoretical data. When it is made into a prototype, its actual torque proves to be lower than its theoretical torque. The actual data presents a promising power efficiency of 32.45%, higher than the usual average range for savonious wind turbines. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Wind turbines

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Relevant Musical Schema-based Human Emotion Controller using Deep Learning Techniques 3

Relevant Musical Schema-based Human Emotion Controller using...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Nagaraj, P. Sudar, K. Muthamil Krishna, Gollamandala Vamsi Yeswanth, Grandhi Srinivas Charan, Gurrala Sai Charan, Nidiganti Kalasalingam Academy of Research and Education Krishnankoil Department of Computer Science and Engineering Virudhunagar India Mepco Schlenk Engineering College Department of Computer Science and Engineering Virudhnagar India

ISBN: (纸本)9798350361186

Musical Schema negotiation describes how a user's facial expressions may convey their emotional state or mood. You may see these expressions in the live video from the system camera. Many efforts are being made to teach machines to recognize various human emotions or moods in the field of computer vision and machine learning (ML). There are several techniques available in machine learning to identify human emotions. We propose a system that detects mood in real time and generates a mood-based music player that recommends songs depending on the observed mood. This is now a function that comes pre-installed on the traditional music player applications on our phones. One major benefit of mood detection integration is customer satisfaction. Among this system's objectives are to analyze the user's look, forecast their expression, and suggest music based on how they seem. © 2024 IEEE.

关键词： Deep learning

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A Deep Learning Approach to Improve Pulmonary Cancer Diagnosis Using CNN 3

A Deep Learning Approach to Improve Pulmonary Cancer Diagnos...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Sudar, K. Muthamil Nagaraj, P. Raghavendra, V. Jaashvitha, V. Jayasri, Y. Reddy, Yerva Dasaradha Ramanjaneya Mepco Schlenk Engineering College Department of Computer Science and Engineering Virudhnagar India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Virudhunagar India

ISBN: (纸本)9798350361186

It has been proposed that physicians use machine learning-based lung cancer prediction algorithms to treat indeterminate pulmonary nodules that are discovered by accident or during screening. These methods might improve decisionmaking, decrease variability in nodule classification, and decrease the proportion of benign nodules that require unnecessary monitoring or investigation. In this work, we summarise the principal lung cancer prediction methods that have been put out so far and discuss some of the advantages and disadvantages of each. We discuss some of the challenges in creating and verifying such methods and outline the path to clinical acceptance. To ensure the best possible quality of input data for the deep learning models, the preprocessing step involves anatomical area segmentation, noise reduction, and image enhancement. The process of feature extraction involves using convolutional neural networks (CNNs) to extract intricate patterns and features from the lung pictures, which are then utilized to diagnose benign and malignant tumors. Typically, a deep neural network or an ensemble of models is used as the classification network to process this information and determine the final diagnosis. A faster and better outcome for patients may result from the recommended pulmonary cancer detection system's ability to assist medical professionals in accurately identifying malignant tumors in lung imaging. Deep learning is projected to be used in lung cancer detection, which will eventually result in earlier and more effective treatment, hence reducing the burden of this fatal condition. © 2024 IEEE.

关键词： Classification (of information)

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